Isolation and Characterization of Four Terpenoidal Compounds with Potential Antimicrobial Activity from Tarconanthus camphorantus L. (Asteraceae).

OBJECTIVES: The objective of this study is to describe the isolation, characterization, and antimicrobial activity of isolated compounds from Tarconanthus camphorantus. MATERIALS AND METHODS: Bioactive compounds such as trifloculoside, parthenolide (sesquiterpene lactones), lupeol, and erythrodiol (pentacyclic triterpens) were isolated from n-hexane extract of T. camphoratus, and their antimicrobial activity against Candida albicans, Escherichia coli, Psuedomonas aeruginosa, Bacillus subtilis, Staphylococcus aureus, and Mycobacterium smegmatis was evaluated. The compounds were characterized using chromatographic and spectroscopic techniques. RESULTS: Trifloculoside, lupeol, and erythrodiol are being reported for the first time from T. camphoratus. The isolated compounds sesquiterpens and lupeol exhibited prodigious antimicrobial activity against B. subtilis and S. aureus with minimum inhibitory concentration values in the range of 25-1000 µg/mL but no activity was observed against other tested organisms, and erythrodiol showed no antimicrobial activity against any of the tested organisms. CONCLUSION: The findings of this study revealed that the new compounds trifloculoside, parthenolide, and lupeol isolated from T. camphoratus exhibited effective antimicrobial potential. It was inferred that T. camphoratus can be effectively used in traditional medicine.

Computer aided detection system for micro calcifications in digital mammograms.

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammogram breast X-ray is considered the most reliable method in early detection of breast cancer. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Micro calcification clusters (MCCs) and masses are the two most important signs for the breast cancer, and their automated detection is very valuable for early breast cancer diagnosis. The main objective is to discuss the computer-aided detection system that has been proposed to assist the radiologists in detecting the specific abnormalities and improving the diagnostic accuracy in making the diagnostic decisions by applying techniques splits into three-steps procedure beginning with enhancement by using Histogram equalization (HE) and Morphological Enhancement, followed by segmentation based on Otsu's threshold the region of interest for the identification of micro calcifications and mass lesions, and at last classification stage, which classify between normal and micro calcifications 'patterns and then classify between benign and malignant micro calcifications. In classification stage; three methods were used, the voting K-Nearest Neighbor classifier (K-NN) with prediction accuracy of 73%, Support Vector Machine classifier (SVM) with prediction accuracy of 83%, and Artificial Neural Network classifier (ANN) with prediction accuracy of 77%.